Hi, I’m Veda Samohitha Chaganti, a Machine Learning and Generative AI Engineer with over 9 years of experience building data-driven and AI solutions across banking, healthcare, e-commerce, and enterprise technology. In my current role at Bank of America, I build AI solutions for compliance, KYC/AML, fraud, risk, and customer-operations teams. My work includes Python, FastAPI, Azure OpenAI, RAG pipelines, LangChain, LangGraph, vector databases, semantic search, document intelligence, and MLOps. I have built systems that help users search policies, summarize documents, extract key information, and receive source-grounded responses securely. Previously, I worked on healthcare AI and ML use cases involving claims analytics, clinical NLP, document processing, patient-risk models, forecasting, and model deployment. I have also worked on recommendation systems, churn prediction, demand forecasting, anomaly detection, and large-scale data pipelines using Python, SQL, PySpark, Databricks, Airflow, Kafka, TensorFlow, PyTorch, Scikit-learn, AWS, Azure, and GCP. What makes me a strong fit for a Machine Learning Engineer role is that I can handle the complete lifecycle: understanding the business problem, preparing data, building and evaluating models, deploying APIs, creating MLOps pipelines, monitoring performance, and working with product, data, and engineering teams to deliver reliable production solutions. I combine strong ML fundamentals with hands-on experience in modern GenAI and cloud-based deployment.…Hi, I’m Veda Samohitha Chaganti, a Machine Learning and Generative AI Engineer with over 9 years of experience building data-driven and AI solutions across banking, healthcare, e-commerce, and enterprise technology. In my current role at Bank of America, I build AI solutions for compliance, KYC/AML, fraud, risk, and customer-operations teams. My work includes Python, FastAPI, Azure OpenAI, RAG pipelines, LangChain, LangGraph, vector databases, semantic search, document intelligence, and MLOps. I have built systems that help users search policies, summarize documents, extract key information, and receive source-grounded responses securely. Previously, I worked on healthcare AI and ML use cases involving claims analytics, clinical NLP, document processing, patient-risk models, forecasting, and model deployment. I have also worked on recommendation systems, churn prediction, demand forecasting, anomaly detection, and large-scale data pipelines using Python, SQL, PySpark, Databricks, Airflow, Kafka, TensorFlow, PyTorch, Scikit-learn, AWS, Azure, and GCP. What makes me a strong fit for a Machine Learning Engineer role is that I can handle the complete lifecycle: understanding the business problem, preparing data, building and evaluating models, deploying APIs, creating MLOps pipelines, monitoring performance, and working with product, data, and engineering teams to deliver reliable production solutions. I combine strong ML fundamentals with hands-on experience in modern GenAI and cloud-based deployment.WWWW…